ENH: Add option to resample data by a non-timeseries column (e.g. Price)

[dsstex]

Is your feature request related to a problem?

Renko Chart Wiki: https://en.wikipedia.org/wiki/Renko_chart

I'm trying to generate a renko chart using the trade tick data. The data contains Timestamp, Price, Volume. The Timestamp is in unix milliseconds format. e.g. 1649289600174.

Pandas already supports OHLC resampling via df.resample('10Min').agg({'Price': 'ohlc'}). However, I would like resample trade data based on price. Not by Time.

Describe the solution you'd like

I'm looking for a solution that would sort of look like

df.resample('10Num').agg({'Price': 'ohlc', 'Timestamp': 'last'}).

Here 10 is the brick size and it is based on the close price. The keyword Num says, treat this as a numeric value resampling instead of timeseries resampling. i.e. If the close price hits +10 or -10, then I would like to aggregate that data.

We should also have a flag to ignore down movement.

if ignore_down set to True, then the agg function should ignore the down side movement. e.g. 100 to 90.

API breaking implications

N/A

Describe alternatives you've considered

At the moment, I'm creating the renko chart manually using a python loop.

Additional context

N/A

[jreback]

this can be done use groupby with a binned grouper

no need to add any api

pls ask on stack overflow usage questions

[dsstex]

@jreback

I did spend many hours in doing research before opening this feature request. If you provide a simple one liner example or a documentation page where I can get more details, that would be helpful for me and other people who stumble upon to this page via google.

Thanks

[jreback]

@dsstex you would need to provide a working example here to illustrate - but in any event this forum is for bug reports or enhancements that fit the api

without an example it's impossible

but i believe what you are doing is possible his groupby

[dsstex]

@jreback

Let me explain my case.

This is how my original data looks like.

Timestamp           Price               Volume

1649289600174       100                 100
1649289600175       102                 150
1649289600176       105                 200
1649289600177       109                 100
1649289600178       110                 200
1649289600179       107                 400
1649289600180       102                 500
1649289600181       101                 600
1649289600182       100                 100
1649289600183       103                 200
1649289600184       107                 400
1649289600185       102                 200
1649289600186        99                 100
1649289600187        97                 100
1649289600188        93                 100
1649289600189        90                 100

After resampling, the result should look like, (Note: I'm using 10 as brick_size here)

1649289600178       110                 750
1649289600182       100               1600
1649289600189         90               1200

I believe you are talking about pd.cut option. And then do groupby.

import pandas as pd
import numpy as np

df = pd.DataFrame({'price': np.random.randint(1, 100, 1000)})
df['bins'] = pd.cut(x=df['price'], bins=[0, 10, 20, 30, 40, 50, 60,
                                          70, 80, 90, 100])

That outputs something like this.

      price       bins
0       92  (90, 100]
1       15   (10, 20]
2       54   (50, 60]
3       55   (50, 60]
4       72   (70, 80]
..     ...        ...
95      88   (80, 90]
96      21   (20, 30]
97      91  (90, 100]
98      51   (50, 60]
99      18   (10, 20]

Please note: As you may well know, trade data is not unique. The price of bitcoin one year back would have looked like 45555 USD. But it's again at the same price this year. If I use a 100 bin size, it would be in (45500, 45600).

A groupby would put both 1 year ago data and current data at the same bin. So I have no idea what is the point of doing groupby here.

I'm looking for a linear implementation. Did I misunderstood your answer or you misunderstood my question?

Thanks

[jreback]

@dsstex pls ask a fully formed question on stack overflow

[dsstex]

@jreback

I have already tried 2 times in stackoverflow and 1 times in data-science stackexchange. Haven't received any answer. Only comments. I had to delete the questions after waiting for multiple days to ask again.

I'll give it a try one more time.

Note: It would be very unprofessional if you force me to move to another forum without understanding my feature request. So I assume the following:

1. You fully understood my feature request
2. It is possible to accomplish what I'm asking in pandas without introducing new API

Thanks

[jreback]

@dsstex you are missing the point

this is NOT a forum for helping you

rather it is a forum for bug reports and feature requests

pandas is all volunteer - please respect everyone's time